from __future__ import absolute_import, print_function, division


import re
import operator
from petl.compat import next, text_type


from petl.errors import ArgumentError
from petl.util.base import Table, asindices
from petl.transform.basics import TransformError
from petl.transform.conversions import convert


def capture(table, field, pattern, newfields=None, include_original=False,
            flags=0, fill=None):
    """
    Add one or more new fields with values captured from an existing field
    searched via a regular expression. E.g.::

        >>> import petl as etl
        >>> table1 = [['id', 'variable', 'value'],
        ...           ['1', 'A1', '12'],
        ...           ['2', 'A2', '15'],
        ...           ['3', 'B1', '18'],
        ...           ['4', 'C12', '19']]
        >>> table2 = etl.capture(table1, 'variable', '(\\w)(\\d+)',
        ...                      ['treat', 'time'])
        >>> table2
        +-----+-------+-------+------+
        | id  | value | treat | time |
        +=====+=======+=======+======+
        | '1' | '12'  | 'A'   | '1'  |
        +-----+-------+-------+------+
        | '2' | '15'  | 'A'   | '2'  |
        +-----+-------+-------+------+
        | '3' | '18'  | 'B'   | '1'  |
        +-----+-------+-------+------+
        | '4' | '19'  | 'C'   | '12' |
        +-----+-------+-------+------+

        >>> # using the include_original argument
        ... table3 = etl.capture(table1, 'variable', '(\\w)(\\d+)',
        ...                      ['treat', 'time'],
        ...                      include_original=True)
        >>> table3
        +-----+----------+-------+-------+------+
        | id  | variable | value | treat | time |
        +=====+==========+=======+=======+======+
        | '1' | 'A1'     | '12'  | 'A'   | '1'  |
        +-----+----------+-------+-------+------+
        | '2' | 'A2'     | '15'  | 'A'   | '2'  |
        +-----+----------+-------+-------+------+
        | '3' | 'B1'     | '18'  | 'B'   | '1'  |
        +-----+----------+-------+-------+------+
        | '4' | 'C12'    | '19'  | 'C'   | '12' |
        +-----+----------+-------+-------+------+

    By default the field on which the capture is performed is omitted. It can
    be included using the `include_original` argument.

    The ``fill`` parameter can be used to provide a list or tuple of values to
    use if the regular expression does not match. The ``fill`` parameter
    should contain as many values as there are capturing groups in the regular
    expression. If ``fill`` is ``None`` (default) then a
    ``petl.transform.TransformError`` will be raised on the first non-matching
    value.

    """

    return CaptureView(table, field, pattern,
                       newfields=newfields,
                       include_original=include_original,
                       flags=flags,
                       fill=fill)


Table.capture = capture


class CaptureView(Table):

    def __init__(self, source, field, pattern, newfields=None,
                 include_original=False, flags=0, fill=None):
        self.source = source
        self.field = field
        self.pattern = pattern
        self.newfields = newfields
        self.include_original = include_original
        self.flags = flags
        self.fill = fill

    def __iter__(self):
        return itercapture(self.source, self.field, self.pattern,
                           self.newfields, self.include_original, self.flags,
                           self.fill)


def itercapture(source, field, pattern, newfields, include_original, flags,
                fill):
    it = iter(source)
    prog = re.compile(pattern, flags)

    hdr = next(it)
    flds = list(map(text_type, hdr))
    if isinstance(field, int) and field < len(hdr):
        field_index = field
    elif field in flds:
        field_index = flds.index(field)
    else:
        raise ArgumentError('field invalid: must be either field name or index')

    # determine output fields
    outhdr = list(flds)
    if not include_original:
        outhdr.remove(field)
    if newfields:
        outhdr.extend(newfields)
    yield tuple(outhdr)

    # construct the output data
    for row in it:
        value = row[field_index]
        if include_original:
            out_row = list(row)
        else:
            out_row = [v for i, v in enumerate(row) if i != field_index]
        match = prog.search(value)
        if match is None:
            if fill is not None:
                out_row.extend(fill)
            else:
                raise TransformError('value %r did not match pattern %r'
                                     % (value, pattern))
        else:
            out_row.extend(match.groups())
        yield tuple(out_row)


def split(table, field, pattern, newfields=None, include_original=False,
          maxsplit=0, flags=0):
    """
    Add one or more new fields with values generated by splitting an
    existing value around occurrences of a regular expression. E.g.::

        >>> import petl as etl
        >>> table1 = [['id', 'variable', 'value'],
        ...           ['1', 'parad1', '12'],
        ...           ['2', 'parad2', '15'],
        ...           ['3', 'tempd1', '18'],
        ...           ['4', 'tempd2', '19']]
        >>> table2 = etl.split(table1, 'variable', 'd', ['variable', 'day'])
        >>> table2
        +-----+-------+----------+-----+
        | id  | value | variable | day |
        +=====+=======+==========+=====+
        | '1' | '12'  | 'para'   | '1' |
        +-----+-------+----------+-----+
        | '2' | '15'  | 'para'   | '2' |
        +-----+-------+----------+-----+
        | '3' | '18'  | 'temp'   | '1' |
        +-----+-------+----------+-----+
        | '4' | '19'  | 'temp'   | '2' |
        +-----+-------+----------+-----+

    By default the field on which the split is performed is omitted. It can
    be included using the `include_original` argument.

    """

    return SplitView(table, field, pattern, newfields, include_original,
                     maxsplit, flags)


Table.split = split


class SplitView(Table):

    def __init__(self, source, field, pattern, newfields=None,
                 include_original=False, maxsplit=0, flags=0):
        self.source = source
        self.field = field
        self.pattern = pattern
        self.newfields = newfields
        self.include_original = include_original
        self.maxsplit = maxsplit
        self.flags = flags

    def __iter__(self):
        return itersplit(self.source, self.field, self.pattern, self.newfields,
                         self.include_original, self.maxsplit, self.flags)


def itersplit(source, field, pattern, newfields, include_original, maxsplit,
              flags):

    it = iter(source)
    prog = re.compile(pattern, flags)

    hdr = next(it)
    flds = list(map(text_type, hdr))
    if isinstance(field, int) and field < len(hdr):
        field_index = field
        field = hdr[field_index]
    elif field in flds:
        field_index = flds.index(field)
    else:
        raise ArgumentError('field invalid: must be either field name or index')

    # determine output fields
    outhdr = list(flds)
    if not include_original:
        outhdr.remove(field)
    if newfields:
        outhdr.extend(newfields)
    yield tuple(outhdr)

    # construct the output data
    for row in it:
        value = row[field_index]
        if include_original:
            out_row = list(row)
        else:
            out_row = [v for i, v in enumerate(row) if i != field_index]
        out_row.extend(prog.split(value, maxsplit))
        yield tuple(out_row)


def sub(table, field, pattern, repl, count=0, flags=0):
    """
    Convenience function to convert values under the given field using a
    regular expression substitution. See also :func:`re.sub`.

    """

    prog = re.compile(pattern, flags)
    conv = lambda v: prog.sub(repl, v, count=count)
    return convert(table, field, conv)


Table.sub = sub


def search(table, *args, **kwargs):
    """
    Perform a regular expression search, returning rows that match a given
    pattern, either anywhere in the row or within a specific field. E.g.::

        >>> import petl as etl
        >>> table1 = [['foo', 'bar', 'baz'],
        ...           ['orange', 12, 'oranges are nice fruit'],
        ...           ['mango', 42, 'I like them'],
        ...           ['banana', 74, 'lovely too'],
        ...           ['cucumber', 41, 'better than mango']]
        >>> # search any field
        ... table2 = etl.search(table1, '.g.')
        >>> table2
        +------------+-----+--------------------------+
        | foo        | bar | baz                      |
        +============+=====+==========================+
        | 'orange'   |  12 | 'oranges are nice fruit' |
        +------------+-----+--------------------------+
        | 'mango'    |  42 | 'I like them'            |
        +------------+-----+--------------------------+
        | 'cucumber' |  41 | 'better than mango'      |
        +------------+-----+--------------------------+

        >>> # search a specific field
        ... table3 = etl.search(table1, 'foo', '.g.')
        >>> table3
        +----------+-----+--------------------------+
        | foo      | bar | baz                      |
        +==========+=====+==========================+
        | 'orange' |  12 | 'oranges are nice fruit' |
        +----------+-----+--------------------------+
        | 'mango'  |  42 | 'I like them'            |
        +----------+-----+--------------------------+

    The complement can be found via
    :func:`petl.transform.regex.searchcomplement`.

    """

    if len(args) == 1:
        field = None
        pattern = args[0]
    elif len(args) == 2:
        field = args[0]
        pattern = args[1]
    else:
        raise ArgumentError('expected 1 or 2 positional arguments')
    return SearchView(table, pattern, field=field, **kwargs)


Table.search = search


class SearchView(Table):

    def __init__(self, table, pattern, field=None, flags=0, complement=False):
        self.table = table
        self.pattern = pattern
        self.field = field
        self.flags = flags
        self.complement = complement

    def __iter__(self):
        return itersearch(self.table, self.pattern, self.field, self.flags,
                          self.complement)


def itersearch(table, pattern, field, flags, complement):
    prog = re.compile(pattern, flags)
    it = iter(table)
    hdr = next(it)
    flds = list(map(text_type, hdr))
    yield tuple(hdr)

    if field is None:
        # search whole row
        test = lambda r: any(prog.search(text_type(v)) for v in r)
    else:
        indices = asindices(hdr, field)
        if len(indices) == 1:
            index = indices[0]
            test = lambda r: prog.search(text_type(r[index]))
        else:
            getvals = operator.itemgetter(*indices)
            test = lambda r: any(prog.search(text_type(v)) for v in getvals(r))
    # complement==False, return rows that match
    if not complement:
        for row in it:
            if test(row):
                yield tuple(row)
    # complement==True, return rows that do not match
    else:
        for row in it:
            if not test(row):
                yield tuple(row)


def searchcomplement(table, *args, **kwargs):
    """
    Perform a regular expression search, returning rows that **do not**
    match a given pattern, either anywhere in the row or within a specific
    field. E.g.::

        >>> import petl as etl
        >>> table1 = [['foo', 'bar', 'baz'],
        ...           ['orange', 12, 'oranges are nice fruit'],
        ...           ['mango', 42, 'I like them'],
        ...           ['banana', 74, 'lovely too'],
        ...           ['cucumber', 41, 'better than mango']]
        >>> # search any field
        ... table2 = etl.searchcomplement(table1, '.g.')
        >>> table2
        +----------+-----+--------------+
        | foo      | bar | baz          |
        +==========+=====+==============+
        | 'banana' |  74 | 'lovely too' |
        +----------+-----+--------------+

        >>> # search a specific field
        ... table3 = etl.searchcomplement(table1, 'foo', '.g.')
        >>> table3
        +------------+-----+---------------------+
        | foo        | bar | baz                 |
        +============+=====+=====================+
        | 'banana'   |  74 | 'lovely too'        |
        +------------+-----+---------------------+
        | 'cucumber' |  41 | 'better than mango' |
        +------------+-----+---------------------+

    This returns the complement of :func:`petl.transform.regex.search`.

    """

    return search(table, *args, complement=True, **kwargs)


Table.searchcomplement = searchcomplement


def splitdown(table, field, pattern, maxsplit=0, flags=0):
    """
    Split a field into multiple rows using a regular expression. E.g.:

        >>> import petl as etl
        >>> table1 = [['name', 'roles'],
        ...           ['Jane Doe', 'president,engineer,tailor,lawyer'],
        ...           ['John Doe', 'rocket scientist,optometrist,chef,knight,sailor']]
        >>> table2 = etl.splitdown(table1, 'roles', ',')
        >>> table2.lookall()
        +------------+--------------------+
        | name       | roles              |
        +============+====================+
        | 'Jane Doe' | 'president'        |
        +------------+--------------------+
        | 'Jane Doe' | 'engineer'         |
        +------------+--------------------+
        | 'Jane Doe' | 'tailor'           |
        +------------+--------------------+
        | 'Jane Doe' | 'lawyer'           |
        +------------+--------------------+
        | 'John Doe' | 'rocket scientist' |
        +------------+--------------------+
        | 'John Doe' | 'optometrist'      |
        +------------+--------------------+
        | 'John Doe' | 'chef'             |
        +------------+--------------------+
        | 'John Doe' | 'knight'           |
        +------------+--------------------+
        | 'John Doe' | 'sailor'           |
        +------------+--------------------+
    
    """

    return SplitDownView(table, field, pattern, maxsplit, flags)


Table.splitdown = splitdown


class SplitDownView(Table):

    def __init__(self, table, field, pattern, maxsplit=0, flags=0):
        self.table = table
        self.field = field
        self.pattern = pattern
        self.maxsplit = maxsplit
        self.flags = flags

    def __iter__(self):
        return itersplitdown(self.table, self.field, self.pattern,
                             self.maxsplit, self.flags)


def itersplitdown(table, field, pattern, maxsplit, flags):

    prog = re.compile(pattern, flags)
    it = iter(table)
    hdr = next(it)
    flds = list(map(text_type, hdr))

    if isinstance(field, int) and field < len(hdr):
        field_index = field
        field = hdr[field_index]
    elif field in flds:
        field_index = flds.index(field)
    else:
        raise ArgumentError('field invalid: must be either field name or index')

    yield tuple(hdr)

    for row in it:
        value = row[field_index]
        for v in prog.split(value, maxsplit):
            yield tuple(v if i == field_index else row[i] for i in range(len(hdr)))

